class_docstr: LexRank: Graph-based Centrality as Salience in Text Summarization
  Source: http://tangra.si.umich.edu/~radev/lexrank/lexrank.pdf
class_docstr_tok: ["LexRank",":","Graph-based","Centrality","as","Salience","in","Text","Summarization","Source",":","http",":","\/\/tangra.si.umich.edu\/~radev\/lexrank\/lexrank.pdf"]
code: def __call__(self, document, sentences_count):
      self._ensure_dependencies_installed()
  
      sentences_words = [
          self._to_words_set(s) for s in document.sentences
      ]
      if not sentences_words:
          return tuple()
  
      tf_metrics = self._compute_tf(sentences_words)
      idf_metrics = self._compute_idf(sentences_words)
  
      matrix = self._create_matrix(
          sentences_words, self.threshold, tf_metrics, idf_metrics
      )
      scores = self.power_method(matrix, self.epsilon)
      ratings = dict(zip(document.sentences, scores))
  
      return self._get_best_sentences(
          document.sentences, sentences_count, ratings
      )
  
file_path: sumy/sumy/summarizers/lex_rank.py
imports: from __future__ import absolute_import
  from __future__ import division, print_function, unicode_literals
  import math
  from collections import Counter
  from ._summarizer import AbstractSummarizer
label_desc: Use image node_id 3 for calling the LexRankSummarizer obj's underlying member method code with example usage: obj.__call__(document, sentences_count) and returns: self, tuple